JAQM Volume 6, Issue 2 - June 30, 2011

Contents

The main goal of this paper is to describe some methods, that make use of employment data and that allow to measure to what the companies are spatially proximate. Specifically, we will outline the most prominent spatial concentration quotients that have been suggested in the literature to analyse the degree to which companies of the same sector are proximate (spatial concentration). We apply the methods on the employment statistics available for Romania's counties.

The study of regional specialization and of concentrating the economic activities contributes to the identification of the place and role of each economic activity within the national economy and its growth potential. Thus, the possibility to emphasize the contribution brought by each economic activity to the development of each region is created. The aim of this paper is to verify relation between the evolution of the regional specialization and geographic concentration of economic activities in eight Romanian development regions. For this purpose, an empirical study of specialization and concentration was performed, at the level of the eight regions of Romania, before and after the moment of integration in the European Union.

This article intends to analyze the position of the villages in the historical region of Dobrogea from the development point of view. With the help of a methodology similar to Human Development Index (HDI) used by the UN, in Romania, in 2009 an inter-institution project (Sandu et al) elaborated the Village Development Index (VDI). Based on this statistic information it can be ascertained that the villages in Dobrogea have a development level superior to the national average. We then built some econometric models to establish the influence factors such as: ethnicity, coast area, delta area, which may have an influence on the development level.

The measure of the passenger satisfaction presents more difficulties, where the customer satisfaction in the public transport sector is subject to different conditions than in other sectors. In this work a strategy based on Rasch analysis and Analysis of Means (ANOM) is proposed. This is based on the idea that the Rasch rating scale model gives 'sufficient statistic' for an underlying unidimensional latent trait as the satisfaction generated by local transport operators. Furthermore, the ability of passengers, measured by the rating scale model, is studied by ANOM decision charts to verify if there are different levels of satisfaction between the different groups of passengers.

When public transport system represents a primary need for citizens, the analysis of users' satisfaction is of the utmost importance to offer and obtain an efficient service. It is clear that a customer will be "satisfied", if his expectations are met and will be" disappointed", if his needs are ignored. In the transport field, the formulation and the definition of organizational and operational criteria are essential requisites to improve service quality. Restoring and improving modes and procedures will certainly guarantee an increasing efficiency, but the evaluation of customer satisfaction has been gaining more and more importance in order to achieve such a goal: different transport companies have been able to set such quality standards not only thanks to their own abilities, but also by taking into account specific service needs, directly expressed by customers through an adequate monitoring process.
With this paper our aim is to make a study that analizes the satisfaction of the Italian population using transport service. With a particular reference to "Buses," "Coaches" and "Trains", we are going to evaluate the proportion of satisfied public transport users according to ISTAT indicators (frequency, punctuality and seats availability) both in regional and in geographical divisions. The methodology used is based on permutation tests.

Standard setting plays an important role in educational and psychological testing. This paper is focused on standard setting using 'cluster analysis' technique. Cluster analysis is a statistical procedure for forming homogenous groups of subjects (examinees). It explores the process of doing cluster analysis and its types are - K-Means and Hierarchical clustering. In the hierarchical cluster analysis, all objects are initially being considered to be a unique cluster. The analysis proceeds sequentially by merging clusters together one step at a time until all objects are merged into a single cluster. In the K-Means cluster analysis, the number of clusters into which the objects which will be portioned is specified initially. The K-means algorithm then establishes the centers of each cluster which are represented by a vector of means (called the cluster centroid) corresponding to the variables used to cluster subjects. The procedure was applied to an achievement test in science. A five cluster solution best separated the examinees according to their proficiency skills. The study concludes that cluster analysis has an edge over other techniques in regard to reducing subjectivity based on expert ratings of items and applicability to performance-based assessments. It does not remove subjectivity from the standard setting process, but does provide subject-matter experts and test developers with a quantitative method for determining different groups of test takers.

Coefficient alpha was first introduced by Lee J. Cronbach in 1951 and since then it continues to serve as a valuable index of reliability within different areas of research. According to the Social Sciences Citation Index, between 1951 and 2010, Cronbach's seminar article (Cronbach, 1951) was cited 6,912 times by other published articles and numerous other publications often cite secondary sources in support of the use of coefficient alpha.